Linear independence of basis vectors

In summary, the conversation discusses the method for proving the linear independence of standard basis vectors. The definition of linear independence is given, along with an example. The method of contradiction is suggested as a way to prove linear independence, and the example of the standard basis in \mathbb{R}^3 is used to illustrate this method. It is then suggested to apply this method to \mathbb{R}^n in general. The conversation ends with a thank you for the clarification.
  • #1
cappygal
9
0
How do I prove the linear independence of the standard basis vectors? My book is helpful by giving the definition of linear independence and a couple examples, but never once shows how to prove that they are linearly independent.
I know that the list of standard basis vectors is linearly independent if:
The only choice of a_1, a_2, ... a_m that makes a_1v_1+a_2v_2+...+a_mv_m=0 is a_1=a_2=...=a_m=0.
But i don't know where to go from there .. any help would be appreciated :confused:
 
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  • #2
Use contradiction. If they were linearly dependent, then by your definition, at least one of the vectors could be written as a linear combination of others. Is this true of the standard basis ?
 
  • #3
For example, in [itex]\mathbb{R}^3 [/itex], the standard basis is:

[tex]\left\{ {\left( {1,0,0} \right),\left( {0,1,0} \right),\left( {0,0,1} \right)} \right\}[/tex]

This basis is linearly independant if, as you say:

[tex]a\left( {1,0,0} \right) + b\left( {0,1,0} \right) + c\left( {0,0,1} \right) = \left( {0,0,0} \right)[/tex]

implies that [itex]a = b = c = 0[/itex].

Well, solve the condition for a, b and c and see if you can find anything else besides the solution we expect.
Can you now see how it will be for [itex]\mathbb{R}^n [/itex] in general?
 
  • #4
Thank you so much .. it actually makes sense now .. Something about being out of school with a broken pelvis means that it's harder to understand what they do in class without you ... thanks!
 
  • #5
No problem :smile:
 

1. What does it mean for basis vectors to be linearly independent?

Linear independence of basis vectors refers to the concept of a set of vectors being able to span the entire vector space without any redundancy or duplication. This means that none of the vectors in the set can be expressed as a linear combination of the other vectors.

2. How is linear independence of basis vectors determined?

To determine if a set of vectors is linearly independent, we can use the definition that no vector in the set can be written as a linear combination of the other vectors. This can be tested using various methods such as Gaussian elimination or the determinant test.

3. Why is linear independence of basis vectors important?

Linear independence of basis vectors is important because it allows us to have a unique and efficient way of representing vectors in a vector space. It also helps us to understand the properties and structure of a vector space.

4. Can a set of basis vectors be linearly dependent?

No, a set of basis vectors cannot be linearly dependent. This is because basis vectors, by definition, are linearly independent and form the basis for the vector space. If a set of basis vectors were linearly dependent, it would not be able to span the entire vector space.

5. How does the concept of linear independence of basis vectors relate to linear transformations?

Linear independence of basis vectors is closely related to linear transformations because it allows us to determine the dimensions and properties of the vector space being transformed. Additionally, linear transformations can change the basis vectors of a vector space, but the linear independence of the basis vectors will remain the same.

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